ADCNN模型：基于TensorFlow的遥感影像水体语义分割训练代码

基于TensorFlow实现ADCNN模型的遥感影像水体语义分割训练代码

1. 导入必要的库和数据集

import tensorflow as tf
from keras.datasets import mnist # 以MNIST为例
from keras.utils import to_categorical

# 加载数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 将标签转换为one-hot编码
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)

2. 定义模型

# 定义输入
inputs = tf.keras.layers.Input(shape=(28,28,1))

# 添加ADCNN模型层
model = ADCNN(img_dim=(28,28,1), nb_classes=10)

# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

3. 训练模型

# 训练模型
model.fit(x_train, y_train, batch_size=32, epochs=10, validation_data=(x_test, y_test))

4. 测试模型

# 评估模型
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

注意：

以上代码框架仅供参考，您需要根据自己的遥感影像数据和任务进行修改。
需要根据实际数据集大小和硬件条件调整batch size、epochs等参数。
需要将MNIST数据集替换为您的遥感影像水体数据集。
需要对模型进行调整，例如添加合适的预处理步骤，设置合适的学习率等。

模型代码：

from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.convolutional import Convolution2D
from keras.layers.pooling import AveragePooling2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.layers import Input
from keras.layers import BatchNormalization
from keras.regularizers import l2
import keras.backend as K

from att_blocks import *
from dense_blocks import *


def ADCNN(img_dim, nb_classes, depth=40, nb_dense_block=3, growth_rate=12, nb_filter=16, dropout_rate=None,
                     weight_decay=1E-4, verbose=True):
  
    n_channels = 64
    model_input = Input(shape=img_dim)

    concat_axis = 1 if K.image_data_format() == 'th' else -1

    assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4'

    # layers in each dense block
    nb_layers = int((depth - 4) / 3)

    # Initial convolution
    x = Convolution2D(nb_filter, (3, 3), kernel_initializer='he_uniform', padding='same', name='initial_conv2D', use_bias=False,
                      kernel_regularizer=l2(weight_decay))(model_input)

    x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay),
                            beta_regularizer=l2(weight_decay))(x)

    # Add attention block
    x = attention_block(x, encoder_depth=1)

    # Add dense blocks
    for block_idx in range(nb_dense_block - 1):
        x, nb_filter = dense_block(x, nb_layers, nb_filter, growth_rate, dropout_rate=dropout_rate,
                                   weight_decay=weight_decay)
        # add transition_block
        x = transition_block(x, nb_filter, dropout_rate=dropout_rate, weight_decay=weight_decay)

    # The last dense_block does not have a transition_block
    x, nb_filter = dense_block(x, nb_layers, nb_filter, growth_rate, dropout_rate=dropout_rate,
                               weight_decay=weight_decay)

    x = Activation('relu')(x)
    x = GlobalAveragePooling2D()(x)
    x = Dense(nb_classes, activation='softmax', kernel_regularizer=l2(weight_decay), bias_regularizer=l2(weight_decay))(x)

    adcnn = Model(inputs=model_input, outputs=x)

    if verbose:
        print('ADCNN-%d-%d created.' % (depth, growth_rate))

    return adcnn

att_blocks.py和dense_blocks.py文件需要根据实际模型结构进行定义。

希望以上代码框架能帮助您构建和训练基于ADCNN的遥感影像水体语义分割模型。